Systems and method for rapid identification and analysis of cells in forensic samples

ABSTRACT

High-throughput methods and systems for using morphological and/or autofluorescence signatures of cells to characterize unknown cell/tissue types within a forensic sample are provided. Machine learning algorithms are used to correlate morphological and/or autofluorescence signatures to characteristics such as cell type.

FIELD OF THE INVENTION

The invention is generally related to systems and methods which enablerapid identification and analysis of cells in forensic samples. Moreparticularly, aspects of the invention utilize autofluorescence and/ormorphological signatures of cells to determine characteristics of theforensic sample such as the contributor(s), cell type, and quantitywithin the sample.

BACKGROUND

Characterizing cells present in biological evidence, such as determiningthe tissue they originated from within the body, can assist with crimereconstructions and downstream DNA profiling methods. Traditionally,caseworking methods for determining tissue source are based onmicrochemical and/or enzymatic reactions targeted toward proteins withinbodily fluids, which have limited sensitivity and/or specificity.Recently, there has been considerable research into biomolecular markersfor tissue identification. These include mRNA transcripts [1],micro-RNAs [2,3], proteomics [4], and DNA methylation patterns [5].Although promising, the specificity of many of these systems is stillbeing investigated and interpretation can require complex bioinformaticworkflows. In particular, microchemical reactions are prone to falsepositive and false negative results and often have large samplerequirements. Genetic tests (based on mRNA or microRNA profiles) canalso be prone to false positive/negative results, consumes some amountof sample, and have not been fully validated for forensic casework.

No known forensic techniques have successfully utilized morphological orintrinsic biochemical differences to differentiate between cells fromdifferent tissues in samples approximating those encountered in forensiccasework. This is likely due to the laborious nature of microscopiccharacterizations and the need for tissue-specific antibody probes whichhave limited success on dried or compromised samples [6,7]. Thus, thereis a need in the art for systems and methods that are able to rapidlyand accurately identify and characterize cells in forensic samples thatovercome the shortcomings of the prior art.

SUMMARY

The present disclosure provides systems and methods that utilize theintrinsic properties of cells for analysis and characterization offorensic samples. The methods are non-destructive in that no biochemicalor immunological stains or probes are required. In some aspects,high-throughput, single cell measurements may be combined with amultivariate classification framework to extract autofluorescent and/ormorphological signatures from biological samples to characterize anddistinguish various cell types within a biological sample.

According to an aspect of some embodiments, a method of characterizingcells from an unknown contributor or contributors in a forensic samplecomprises obtaining a plurality of morphological and/or autofluorescencemeasurements from a plurality of cells in the forensic sample; andclassifying the plurality of cells into three or more groups using twoor more binary classifications.

According to an aspect of some embodiments, a binary classificationcomprises calculating at least two coordinate values for each cell usingrespective first and second functions that are weighted combinations ofthe plurality of morphological and/or autofluorescence measurements,comparing the at least two coordinate values calculated for each cellagainst a distribution of coordinate values in a reference dataset, andsorting each cell into either a first group or a second group based onthe comparison, wherein the three or more groups includes the firstgroup and/or second group.

According to an aspect of some embodiments, two or more binaryclassifications are performed successively, wherein for any two binaryclassifications in immediate succession, only the cells sorted into thesecond group by the first binary classification are subjected to thesecond binary classification.

According to an aspect of some embodiments, the respective functions ofthe two or more binary classifications contain the same plurality ofmorphological and/or autofluorescence variables but differentweightings.

According to an aspect of some embodiments, the comparison stepcomprises comparing ratios of multivariate distances between thecalculated coordinate values and multivariate centroids of cell groupsin the reference dataset.

According to an aspect of some embodiments, the three or more groupsinto which cells are classified are all epithelial cell types. In someembodiments the three or more classification groups comprise epidermal,buccal, and vaginal.

According to an aspect of some embodiments, the two or more binaryclassifications include a first binary classification thatdifferentiates epidermal cells from non-epidermal cells and a secondbinary classification that differentiates buccal cells from vaginalcells, wherein the second binary classification is performed only forcells classified by the first binary classification as non-epidermalcells.

According to an aspect of some embodiments, a method comprises countinga total cell count for each of the final classification groups after allbinary classifications are complete.

According to an aspect of some embodiments, the step of obtaining maycomprise generating images of individual cells and analyzing the imagesto obtain the plurality of morphological and/or autofluorescencemeasurements. In some embodiments the measurements are obtained with animaging flow cytometer.

According to an aspect of some embodiments, the one or moremorphological and/or autofluorescence measurements are selected from thegroup consisting of area, aspect ratio, aspect ratio intensity,contrast, intensity, mean pixel, median pixel, max pixel, length, width,height, brightness detail intensity (‘R3’ pixel increment), raw centroidX, raw centroid Y, and circularity. The one or more knowncharacteristics may be selected from the group consisting of cell type;time since cell deposition; and age, sex, and ethnicity of cellcontributor.

According to an aspect of some embodiments, a method of training acomputer for analysis of forensic samples comprises obtaining for aplurality of morphological and/or autofluorescence variables a pluralityof measurements from a plurality of cells having one or more knowncharacteristics; and generating two or more functions which are weightedcombinations of the plurality of morphological and/or autofluorescencevariables such that the variation between user-defined sample groups ismaximized and within group variation is minimized.

DESCRIPTION OF THE DRAWINGS

FIG. 1. Diagram of a rapid forensic cell testing process according toexample embodiments of the disclosure.

FIG. 2. Image gallery for three epithelial cell tissue sources. IFCbrightfield images for buccal cells (columns 1-3), epidermal cells(columns 4-6), and vaginal cells (columns 7-9). Each image frame is 50μm×50 μm. Object identifiers are included with each image.

FIG. 3. Discriminant Function Analysis (DFA) of epithelial cells fromthree tissue sources using IFC variables. The first discriminantfunction (x-axis) accounted for ˜74% of the between group variation andthe second discriminant function (y-axis) accounted for ˜26%.

FIGS. 4A and 4B. DFA of buccal cell populations from the same donor (A:I66 and B: L49) aged for different amounts of time.

FIGS. 5A and 5B. DFA of cell populations derived from three differentcontributors for buccal (A) and epidermal (B) tissue sources. Buccalcells were dried for 48 hours at room temperature and epidermal cellsamples were dried for 24 hours at room temperature prior to analysis.

FIG. 6. Flow diagram of exemplary method for characterizing a pluralityof cells, in particular classifying a population of cells by cell/tissuetype.

FIG. 7A. Block diagram of process for discriminating/classifying imagesof cells into a three different tissue/cell types.

FIG. 7B. Block diagram of process for classifying images of cells intoany number of different tissue/cell types.

FIG. 8. An exemplary device and system according to some embodiments.

DETAILED DESCRIPTION

Cells obtained from different subjects and different tissues or cellssubject to different environmental conditions may have intrinsicbiochemical, structural, and morphological variances. Embodiments of thedisclosure provide high-throughput, non-destructive methods that usethese variances to identify cell types, determine the age of an evidencestain, and infer phenotypic attributes of contributors in forensicbiological samples. For example, some embodiments are especiallywell-suited for identification of epithelial cell types. Since theintrinsic properties of cells are being analyzed, no biochemical orimmunological stains or probes are required. As discussed in theExample, multivariable classification frameworks may be used todistinguish and characterize unknown cell populations of a forensicsample with an overall high degree of accuracy.

FIG. 1 is a diagram of a process 100 for rapid forensic cell testing andanalysis according to some exemplary embodiments. At a high level, theprocess includes sample collection 101, imaging 102, feature extraction103, and statistical analysis and identification 104. Sample collection101 refers to the obtaining physical cells, e.g., from a crime scene orrape victim. Imaging 102 comprises obtaining images (e.g., image data)of individual cells. Feature extraction 103 comprises obtainingmeasurements for a plurality of morphological and/or autofluorescencevariables. Statistical analysis and identification 104 comprisesemploying the extracted features to make conclusions and inferencesabout the sampled cells. Block 104 may comprise cell type identificationand cell quantification with respect to each cell type, for example. Theuse of high-throughput technology allows for rapid (e.g. less than 5minutes), non-destructive, and quantitative sample analysis.

Many if not all of the features may be obtained from the images of block102 and are thus non-destructive with respect to the original samplesfrom block 101. This contrasts with certain analyses such as DNAanalysis which use and destruction of the original cells. Absent contextwhich indicates otherwise, this disclosure generally refers to cells andcell images interchangeably, with an understanding that the measurementsare generally obtainable and indeed obtained from images of the cells,but the measurements ultimately characterize properties of the actualcells. A significant exception to this generalization is that somemeasurements are inextricably linked to both the original cell and theimaging technique. For example, a particular wavelength may be used totake a measurement, and a different wavelength may result in a differentoutcome. In such cases those of ordinary skill in the art will recognizethat both the original cell being characterized and the imagingtechnique are underlying elements of the measurement or variable.

The fluorescent or morphological properties of cells may be identifiedand measured using any microscopy method known in the art, includingfield-portable technology. Typically, microscopes compatible with themethods of the disclosure include a camera for capturing images of cellsand a processor for determining and analyzing fluorescent ormorphological properties. In some embodiments, commercial or open sourcesoftware platforms (e.g., [21]) are utilized.

FIG. 2 shows actual cell images according to known photographic methodsfor imaging cells at the time of the invention. Imaging techniques andtechnologies already in existence, presently under development, or notyet developed may be employed in different embodiments consistent withthe disclosure herein. In other words, embodiments of the invention arenot limited by the current state of the art of cell imaging technology,absent an express recitation in claims otherwise. The illustrativeimages of FIG. 2 are of actual individual cells imaged by a microscope.In some embodiments, the microscope is an imaging flow cytometer (IFC)which combines conventional flow cytometry analysis whereby the opticalproperties of individual cells are interrogated with lasers at setwavelengths (e.g. wavelengths from 300-750 nm) with fluorescence andbright field imaging of those same cells. IFC is routinely used inbiomedical and clinical research for identification of unusual celltypes as well as high resolution surveys of both cellular andsub-cellular processes [8]. The primary advantage of IFC overconventional microscopic analysis is that images of single cells arecollected in a high throughput manner (as many as hundreds per second)and at multiple fluorescence channels simultaneously. The resultingmultivariate data streams can therefore be used to compare profilesbetween individual cells or between larger populations. An exemplarycommercially available IFC is the Amnis® Imagestream X Mark II imagingflow cytometer (EMD Millipore; Burlington, Mass.) equipped with 405 nm,488 nm, 561 nm, and 642 nm lasers.

In some embodiments, data obtained from individual cells includefeatures selected from, but not limited to, area, aspect ratio, aspectratio intensity, contrast, intensity, mean pixel, median pixel, maxpixel, length, width, height, brightness detail intensity (‘R3’ pixelincrement), raw centroid X, raw centroid Y, and circularity. In someembodiments, a plurality (i.e. two or more) of these variables and/oradditional variables and/or alternative variables may be used. (e.g.,Brightness detail intensity in R7 pixel increment, elongatedness,compactness). In some embodiments, Fluorescence Intensity, BrightnessDetail Intensity, Max Pixel Intensity, and Circularity appearparticularly influential in classifying a cell correctly. In someembodiments, one or more of the aforementioned feature measurements areused for training a series of algorithms as described herein or forcharacterizing an unknown sample. These feature measurements may becollected across multiple, e.g. 2, 3, 4, 5, 6, or more, detectorchannels (e.g., fluorescence and brightfield wavelengths). Somemeasurements, such as centroid X/Y and circularity, may be determinedusing only brightfield images.

FIG. 3 shows an exemplary type of graphical depiction available as anoutput to the process 100 of FIG. 1. Embodiments of the disclosure useimaging techniques to determine the autofluorescence and/ormorphological signatures of cells in a sample. The extracted featuresare used to identify, for example, cell type and cell number. In FIG. 3,each point represents a single cell/image of a cell. The x- and y-axesare different functions which each yield a single numerical value forcharacterizing a given cell based on the extracted features. Thesefunctions are configured so that cells of the same type cluster whenplotted, and cells of different types do not cluster when plotted. Insome embodiments, the signatures allow for distinguishing betweendifferent types of epithelial cell types. FIG. 3 clearly shows theseparation of three different cell types, namely buccal, epidermal, andvaginal. The generation of the functions for the axes of the plot inFIG. 3 is discussed in greater detail below in connection with FIGS. 6,7A, and 7B. The use of high-throughput technology allows for rapid (e.g.less than 5 minutes), non-destructive, and quantitative sample analysis.

FIGS. 4A, 4B, 5A, and 5B are additional plots of cells in which thesampled cells and/or functions used for the axes have been modified. Aswith the plot in FIG. 3, cells sharing a certain common characteristic(e.g., cell type) are clustered together, whereas cells having certaindifferent characteristics are more distant and belong to differentclusters. These figures will be discussed in greater detail in theExample below.

Cell types that may be identified using methods of the disclosureinclude, but are not limited to buccal cells, vaginal cells, epidermal,and other skin or epithelial cells, and blood cells. The cells may beobtained from a forensic sample, such as a “touch” or “contact” sampleleft when a person touches a surface. Other sample types from whichcells may be obtained include, but are not limited to, blood, urine,vaginal, semen, saliva, and hair samples. The methods of the presentdisclosure allow for the analysis of cells in such samples even whengenetic material is not recoverable.

FIG. 6 is a flowchart for an exemplary method 600 for characterizing aplurality of cells from an unknown contributor or contributors in aforensic sample. Contributors may be persons or parts thereof, such asorgans (e.g., skin, vagina, anus, etc.). Generally, the method 600 maybe used for classifying the plurality of cells into three or more groupsusing two or more binary classifications 610 which may be tiered orperformed successively. FIGS. 7A and 7B, discussed below, providefurther illustration of the tiered/successive implementation.

At block 601 reference measurements are obtained as a basis for theclassification 610. The reference measurements are of a population orpopulations of cells of known type(s). At block 602 the referencemeasurements are used to generate functions for binary classification.At block 614 the reference measurements may then be inserted into thefunctions to produce a reference dataset containing a distribution ofcoordinate values. With blocks 601, 602, and 614 completed, aclassification 610 may proceed.

Measurements for cells of unknown origin or contributor are obtained atblock 613 and serve as the base input for a classification 610. A cell'simage may be analyzed, and measurement data extracted, to determinevalues for variables such as area of cell, average pixel density,contrast, and aspect ratio, among others.

Generally, a single binary classification 610 comprises i) at block 603,calculating at least two coordinate values for each respective cell ofunknown origin using the measurements of block 613 inserted intorespective first and second functions of block 602, ii) at block 604,comparing the at least two coordinate values calculated for each cellagainst the reference dataset distribution of block 614, and iii) atblock 605, sorting each cell into either a first group or a second groupbased on the comparison of block 604.

The comparison step at block 604 may comprise comparing ratios ofmultivariate distances between the calculated coordinate values andmultivariate centroids of cell groups in the reference dataset. Cellswhich are being sorted may be plotted on the same plots as referencedataset cell populations. A reference dataset cell population may form adata point cluster which is characterized or characterizable with acentroid. A distance from the point of a cell being sorted to areference dataset centroid may be determined. The distance may bemultivariate (e.g., determined based on multiple cell characteristicsusing multiple classification functions). A determination may be made ofwhether the distance exceeds or does not exceed a predeterminedthreshold. Whether the threshold is or is not exceeded may be used todetermine whether the cell being sorted is or is not related to theknown cells of the reference dataset cluster/centroid. Comparisons ofdistances and/or thresholds may be expressed as ratios, e.g., ratios ofmultivariate distances between calculated coordinate values for cellsbeing sorted and multivariate centroids of cell groups in a referencedataset. The sorting step at block 605 may include both a groupclassification for a given cell plus a probability estimate for theaccuracy of that classification.

Some cells may require only one binary classification 610, while othersmay require two, three, four, or more successive classifications. Eachbinary classification 610 sorts a cell into one of two groups. In anexemplary method 600, the first group is a final group meaning sortingis complete for cells in that group. The second group may also be afinal group, or the second group may require further processing byreprocessing with blocks 603, 604, and 605, this time with a differentset of functions produced by block 602. For every final group, a totalcell count/abundance may be calculated at block 607. Once the inputcells (cell images) have been sorted into final groups, information isoutput at block 608. Block 608 may also be a continuous output processthat updates output information as the process 600 is underway.

The output information of block 608 pertains to the three or more groupsand is based on the classification 610. For example, the outputinformation may include what final groups were used to classify, howmany cells were sorted into each final group, what percentage of thecells from the input population of cells were sorted into each finalgroup, classification accuracy probabilities for respective cells orgroups of cells, and/or other information. The information output atblock 608 may be output to a downstream computer/computer system/user,such as personnel or systems that use the information for downstream DNAprofiling and/or crime reconstruction. The information output at block608 may be output to a human user or a machine user, for example.Outputting may comprise or consist of printing, displaying on a screenor other display device, supplying an audio output, an electronic datatransfer (wired or wireless), or some other means of output. Outputinformation may be or include plots of the calculated coordinates withor without plotted points of the reference dataset. Such plots may beprinted or displayed, for example.

FIG. 7A presents a process 700 for discriminating/classifying images ofindividual cells into a plurality of tissue/cell typecategories/classifications/groups. The process 700 corresponds with themethod 600 of FIG. 6 but uses specific cell types for illustrativepurposes. The process 700 addresses the need to classify cells, orimages 701 thereof, into a plurality of distinct groups, in particularexactly three final groups: epidermal, buccal, and vaginal. At theoutset a population of cells or collection of cell images 701 areavailable, but the specific tissue or cell type characterizing eachindividual cell or image is unknown. For instance, the plurality ofcells 701 may in fact be all buccal, all vaginal, all epidermal, acombination of buccal and vaginal, a combination of vaginal andepidermal, a combination of buccal and epidermal, or a combination ofall three types.

The images 701, in particular measurements obtained therefrom (block 601of FIG. 6) are used to calculate coordinate values for a set offunctions of a first binary classification 703 (block 603 of FIG. 6).The calculated values, after a comparison with a reference datasetdistribution (block 604 of FIG. 6), are sorted into a (final) epidermalgroup 721 or into a (non-final) non-epidermal group 722. Only the cellssorted into the non-epidermal group 722 are subjected to the successiveor subsequent tier of binary classification functions 705. The secondbinary classification sorts all input cells into either the (final)buccal group 723 or (final) vaginal group 724.

The sets of classification functions 703 and 705 are weightedcombinations of the plurality of morphological and/or autofluorescencevariables 707. These variables may be pre-selected, e.g., by a humanuser, program, or device. The available variables are extensive and arediscussed elsewhere in this disclosure. The weighted combinations may belinear sums. Accordingly a single classification function requires botha plurality of variables and a plurality of weights (generally, a singleweight for each respective variable).

Weights may also be referred to as coefficients. For any given set ofclassification functions 703 or 705, the weights may be determined usingone or more machine learning techniques (i.e., training 709) and areference dataset 711. (Note that block 601 of FIG. 6 generallycorresponds with block 701 of FIG. 7. Block 602 of FIG. 6 generallycorresponds with both blocks 703 and 705 of FIG. 7.)

Training 709 (e.g., function generation block 602 of FIG. 6) determinesweights using one or more of linear discriminant analysis, one or moreartificial neural networks, and hierarchical clustering. For someembodiments a preferred means of generating classification functions isusing a discriminant function analysis statistical approach wherebymultivariate differences between cell type groups are maximized andmultivariate differences within groups are minimized.

Generally, more variables 707 yield more accurate function sets 703 and705. However, a minimum group of variables may be used to achieve anadequate result without undue processing burden. The minimum variableset, according to some exemplary embodiments, consists of FluorescenceIntensity, Brightness Detail Intensity, Max Pixel Intensity, andCircularity. Generally, however, a variable set will include thesevariables among others. In some instances the training 709 may give oneor more variables a weight of close to zero or zero (e.g., −0.01 to0.01, −0.001 to 0.001, 0). In some instances the training 709 givesevery variable which is provided as an input a non-zero weight, eitherpositive or negative.

Using a reference dataset and a training methodology such asdiscriminant function analysis as discussed above, classificationfunctions are generated which may have appear as follows:

Coordinate #1=(−0.2*Area of cell in fluorescence channel 1)+(−0.1*Areaof cell in brightfield channel)+(0.6*Aspect ratio in brightfieldchannel)+(0.1*Intensity of fluorescence in Channel 2)+(0.3*Intensityfluorescence in Channel 3)+(−0.2*Circularity of cell)+ . . .

Coordinate #2=(−0.7*Area of cell in fluorescence channel 1)+(−0.07*Areaof cell in brightfield channel)+(0.2*Aspect ratio in brightfieldchannel)+(0.04*Intensity of fluorescence in Channel 2)+(0.1*Intensityfluorescence in Channel 3)+(−0.6*Circularity of cell)+ . . .

Of course the variables used in the equations depends on the variables707 which are pre-selected, and the weight associated with eachrespective variable depends on the reference dataset employed in a givenembodiment. As discussed above, the reference dataset is a population ofcells or collection of cell images for which the cell or tissue type ofeach cell or cell image is already known. For example, the referencedataset may contain a collection of epithelial cell images which areeach respectively known to be either buccal, vaginal, or epidermal. Asingle reference dataset may be used for a variety of cell images to beclassified. Alternatively, different embodiments may employ differentreference datasets.

Though the above description of FIGS. 6 and 7A has been described usingbuccal, vaginal, and epidermal as the candidate cell types for whichcells may be classified, the algorithm may be adapted or modified basedon the teachings herein for applicability to additional or alternativecell types.

FIG. 7B adapts process 700 for any number N of classification groups,where N may be 2, 3, 4, 5, 6, 7, 8, 9, 10, or more. The total number ofsets of binary classification functions will then be N−1. Each binaryclassification yields one final group of sorted cells and a non-finalgroup, with the exception of the final binary classification whichyields two final groups.

Different combinations of cellular measurements may maximizeclassification accuracy particularly for cell types with similarbiochemical and physical properties but different source tissue. Forexample, differences in red autofluorescence emissions (e.g. 650-670 nm)may be used to first identify and separate cells deposited by differentcontributors. Once separated, one or more morphological features maythen be analyzed to determine, e.g. the cell type. In an exemplaryembodiment, one or more of the size, circularity, intensity, andbrightness detail of the cells may be used to distinguish betweenbuccal, vaginal, and epidermal cells (see Example). In some embodiments,the overall size of the cells are used to distinguish between male andfemale contributors and/or between younger and older contributors. Forexample, female contributors generally have larger cells than malecontributors and older contributors generally have larger cells thanyounger contributors.

Since most microscopic imaging methods (including IFC) are inherentlynon-destructive techniques, some embodiments of the disclosure allow forcollecting and non-destructively classifying cells according to celltype before analysis for DNA profiling or other biologicalcharacterizations which may destroy the cells. Standard DNA profilingtechniques may be used in conjunction with or supplemental to themethods disclosed herein, e.g. to increase the probative value ofevidence and/or increase sample processing efficiency by identifyingsamples likely to provide greater DNA yield. This is due to the factthat intracellular DNA content characteristically varies across manytissue types (e.g., between differentiated epidermal cells and buccalcells); therefore identifying cell types and quantifying their abundancecan often predict the DNA yield and the quality of the DNA profilelikely to be produced from the sample. In some forensic sample types,the number of cells present will be proportional to the amount of DNArecovered. Additionally, some cell types have characteristically higherlevels of intracellular DNA than others. For example, intact buccal andvaginal cells (that are nucleated) will have much more DNA in them thanan epidermal cell. So detecting the presence of high levels of buccalcells in one sample over another can indicate that the sample willprovide more DNA. In some embodiments, the flow cytometer is configuredand used to determine the quantity or relative abundance of cells in asample.

Identifying the presence of one or more of cell types in a biologicalsample when combined with DNA profiling results may be useful whenevaluating either single source or mixture samples to explain thepresence (or absence) of particular individuals' DNA (e.g., claims ofsexual assault versus denial of such activity and suggestions ofindirect transfer). Additionally, because DNA yield has been observed tosystematically vary between epidermal cells and other types ofepithelial tissue [9], determining the presence and relative quantitiesof each cell type can help direct downstream DNA profiling efforts.

During forensic casework, samples are often collected at differentlengths of time after deposition and/or stored for different lengths oftime prior to analysis. Some methods of the present disclosure allow fordetermining cell type (e.g., epithelial cell type) for cells of variousages, e.g., hours (e.g. 1-24 or more hours), days (e.g. 1-7 or moredays), weeks (e.g. 1-5 or more weeks), months (e.g. 1-12 or moremonths), or years (e.g. 1-10 or more years).

Some embodiments may include algorithms configured for determining the“age” of a forensic sample, e.g. the time since a touch sample wasdeposited. As shown in the Example, fluorescent and/or morphologicalfeatures of cells may change in a characteristic way over time.Depending on time from deposition to collection, and from collection toanalysis, samples may be “aged” or “dried” for hours (e.g. 1-24 or morehours), days (e.g. 1-7 or more days), weeks (e.g. 1-5 or more weeks),months (e.g. 1-12 or more months), or years (e.g. 1-10 or more years).The methods of the present disclosure may also be used to distinguishsamples of different ages, e.g. between two or more samples deposited atthe same location or scene at different times.

In addition to characterizing cells from an unknown contributor(s),embodiments of the disclosure provide methods for training a computerfor such analysis. A database or library of cell fluorescence andmorphological signatures may be created using cell samples having knowncharacteristics. According to an exemplary embodiment, an imaging flowcytometer is used to obtain microscopic images of individual cells takenat multiple fluorescent wavelengths as well as standard brightfieldillumination. A series of measurements are then made on individualcells, e.g., area, length, aspect ratio, fluorescence intensity, etc.using a suitable software platform. Such platforms include, for example,commercial software (e.g., IDEAS® analysis software) and open sourcescripts (e.g., ‘Cell Profiler’ platform). Machine learning algorithmsmay then be applied to correlate those measurements with the known cellcharacteristics, such as the cell type (e.g. saliva, vaginal, blood,epidermal), and to develop a predictive framework for identifying cellcharacteristics in a blinded/unknown forensic sample.

In some embodiments variables may be explicitly excluded, e.g. by userselection input. As an alternative to outright exclusion, some variablesmay be included for analysis but attributed a weight of zero or nearnero (e.g., e.g., −0.01 to 0.01, −0.001 to 0.001, 0). Variables whichmay be collected from cell images yet excluded from analysis forpurposes of cell type classification. The reason for exclusion of agiven variable may be because the variable varies with factors that arenot intrinsic to the cells. For example a variable called ‘rawfluorescence intensity’ may be collected but is affected by fluctuationsin the intensity of the laser and fluorescence of non-biologicalparticles that may be present in the solution (i.e., not cellular).

The precise number of variables which may be employed may differ someamong embodiments. Different types of geometric or fluorescencemeasurements (area, aspect ratio, fluorescence intensity, etc.) may eachbe measured at different wavelengths (e.g., six different wavelengths).Accordingly, an “area measurement,” for example, may actually refer tosix different measurements (Area at the first wavelength, area in thesecond wavelength, etc.). The same is true of other cellcharacteristics. Though some embodiments may employ as few as 10 or 20variables, some exemplary embodiments use between 50 and 100, or 100 to200, or more than 200 variables. A single “variable” may be for aspecific characteristic of a cell regardless of the measuring techniqueor it may be a specific characteristic associated with a particularmeasuring technique. For instance, an exemplary embodiment may use˜150-180 variables representing 30 measurements in up to 6 fluorescentwavelengths each.

FIG. 8 is a block diagram of an exemplary device 801 or system 802 forcarrying out embodiments, e.g., methods and processes discussed above.Generally a device 801 may be a computer or multiple computers. A device801 may generally comprise one processor 806 (or multiple processors),transitory memory 808, non-transitory memory 810, and input/outputdevice or devices 811. Other elements may also be included (e.g., powersystem elements) but are not illustrated. Algorithms and processes ofembodiments such as described herein may be generated and/or stored witha device 801 (e.g., generated with a processor 806, stored with storage810). The device 801 may itself be an IFC, for example, or may have data(e.g., image data) supplied to it by an IFC (e.g., by a wiredconnection, wireless connection, and/or over a network). A system 802may employ multiple devices 801 why may send, receive, and/or exchangedata over a network 812 or by some other means known in the art.

The stored values or ranges of values for various cell characteristicsmay be on a database (e.g., a non-transient computer readable medium810) that is on or accessible to a single computer or may be storedseparately on different computers within a network. In one embodiment,cells from a forensic sample are imaged using the imaging flow cytometerand measurements taken from the images are compared to stored values orranges of values associated with certain cell characteristics. Matchesfrom this comparison are output to the user, preferably on an automatedbasis. The testing may take place on the order of seconds to minutes(depending on the number of measurements desired), and thedeterminations, comparisons and output may take place on the order ofseconds to minutes depending on the number of values to be computed andcompared and the number of different stored values or value ranges to beconsidered.

To test a trained computer's ability to accurately assess the proportionof cell types in a sample having more than one cell contributor,simulated mixtures may be created by randomly sampling two or moredonors' cell images. These images may then be classified into cell typesusing the remaining contributor cell populations as the referencedataset for discriminant function analysis (DFA). No humaninterpretation is necessary to reach the final cell classification.

The exemplary systems and methods described herein may be used by anyforensic caseworking agency that processes biological evidence, e.g. forDNA profiling. This includes federal agencies, forensicservice/consulting firms or laboratories, and state and local crimelaboratories.

It will be readily apparent to one of ordinary skill in the art that thevarious processes described herein may be implemented by, e.g.,appropriately programmed general purpose computers, special purposecomputers and computing devices. Typically a processor (e.g., one ormore microprocessors, one or more microcontrollers, one or more digitalsignal processors) will receive instructions (e.g., from a memory orlike device), and execute those instructions, thereby performing one ormore processes defined by those instructions. Instructions may beembodied in, e.g., one or more computer programs, one or more scripts.

Within this application, the term “processor” or “computer” means one ormore microprocessors, central processing units (CPUs), computing devices(e.g. desk top computer, lap top computers, tablets, personal dataassistants, smart phones, dongles, etc.), microcontrollers, digitalsignal processors, or like devices or any combination thereof,regardless of the architecture (e.g., chip-levelmultiprocessing/multi-core, RISC, CISC, Microprocessor withoutInterlocked Pipeline Stages, pipelining configuration, simultaneousmultithreading). The system and method of this invention may beimplemented on a single computer, a network of computers, or by cloudcomputing across one or multiple networks whereby the systems andnetworks can deliver the software which implements the system and methodas a service.

Similarly, a description of a process is likewise a description of anapparatus for performing the process. The apparatus that performs theprocess can include, e.g., a processor and those input devices andoutput devices that are appropriate to perform the process. Programsthat implement such methods (as well as other types of data) may bestored and transmitted using a variety of media (e.g., computer readablemedia) in a number of manners. In some embodiments, hard-wired circuitryor custom hardware may be used in place of, or in combination with, someor all of the software instructions that can implement the processes ofvarious embodiments. Thus, various combinations of hardware and softwaremay be used instead of software only.

The term “computer-readable medium” refers to any medium, a plurality ofthe same, or a combination of different media that participate inproviding data (e.g., instructions, data structures) which may be readby a computer, a processor or a like device. Such a medium may take manyforms, including but not limited to, non-volatile media, volatile media,and transmission media. Non-volatile media include, for example, opticalor magnetic disks and other persistent memory. Volatile media includedynamic random access memory (DRAM), which typically constitutes themain memory. Transmission media include coaxial cables, copper wire andfiber optics, including the wires that comprise a system bus coupled tothe processor. Transmission media may include or convey acoustic waves,light waves and electromagnetic emissions, such as those generatedduring radio frequency (RF) and infrared (IR) data communications.Common forms of computer-readable media include, for example, a floppydisk, a flexible disk, hard disk, magnetic tape, any other magneticmedium, a CD-ROM, DVD, any other optical medium, punch cards, papertape, any other physical medium with patterns of holes, a RAM, a PROM,an EPROM, a FLASH-EEPROM, any other memory chip or cartridge, a carrierwave as described hereinafter, or any other non-transient computerreadable medium from which a computer can read. Various forms ofcomputer readable media may be involved in carrying data (e.g. sequencesof instructions) to a processor. For example, data may be (i) deliveredfrom RAM to a processor; (ii) carried over a wireless transmissionmedium; (iii) formatted and/or transmitted according to numerousformats, standards or protocols, such as Ethernet (or IEEE 802.3), SAP,ATP, Bluetooth, and TCP/IP, TDMA, CDMA, and 3G/4G/LTE; and/or (iv)encrypted to ensure privacy or prevent fraud in any of a variety of wayswell known in the art.

Output from the automated system and method may be provided to an outputdevice which can take any form suitable for its intended purpose, and beprovided to a printer, a display, a computer or network of computers,and may provide visual or audible signals which can be discerned by auser. For example, the computer(s) or network of computers used forprocessing information from the imaging flow cytometer may be directlyor remotely connected to the imaging flow cytometer and may be incommunication (wireless or wired) over a network such as the Internet.

It is to be understood that this invention is not limited to particularembodiments described, as such may, of course, vary. It is also to beunderstood that the terminology used herein is for the purpose ofdescribing particular embodiments only, and is not intended to belimiting, since the scope of the present invention will be limited onlyby the appended claims.

Where a range of values is provided, it is understood that eachintervening value, to the hundredth of the unit of the lower limitunless the context clearly dictates otherwise, between the upper andlower limit of that range and any other stated or intervening value inthat stated range, is encompassed within the invention. The upper andlower limits of these smaller ranges may independently be included inthe smaller ranges and are also encompassed within the invention,subject to any specifically excluded limit in the stated range. Wherethe stated range includes one or both of the limits, ranges excludingeither or both of those included limits are also included in theinvention.

All publications and patents cited in this specification are hereinincorporated by reference as if each individual publication or patentwere specifically and individually indicated to be incorporated byreference and are incorporated herein by reference to disclose anddescribe the methods and/or materials in connection with which thepublications are cited. The citation of any publication is for itsdisclosure prior to the filing date and should not be construed as anadmission that the present invention is not entitled to antedate suchpublication by virtue of prior invention. Further, the dates ofpublication provided may be different from the actual publication dateswhich may need to be independently confirmed.

It is noted that, as used herein and in the appended claims, thesingular forms “a”, “an”, and “the” include plural referents unless thecontext clearly dictates otherwise. It is further noted that the claimsmay be drafted to exclude any optional element. As such, this statementis intended to serve as antecedent basis for use of such exclusiveterminology as “solely,” “only” and the like in connection with therecitation of claim elements, or use of a “negative” limitation.

As will be apparent to those of skill in the art upon reading thisdisclosure, each of the individual embodiments described and illustratedherein has discrete components and features which may be readilyseparated from or combined with the features of any of the other severalembodiments without departing from the scope or spirit of the presentinvention. Any recited method can be carried out in the order of eventsrecited or in any other order which is logically possible.

Example. Rapid Differentiation of Epithelial Cell Types in AgedBiological Samples Using Autofluorescent and Morphological SignaturesAbstract

Establishing the tissue source of cells within a biological sample is animportant capability for forensic laboratories. In this study, ImagingFlow Cytometry (IFC) was used to analyze individual cells recovered frombuccal, epidermal, and vaginal samples that had been dried between 24hours and more than eight weeks. Measurements capturing the size, shape,and fluorescent properties of cells were collected in an automatedmanner and then used to build a multivariate statistical framework fordifferentiating cells based on tissue type. Results showed thatepidermal cells could be distinguished from vaginal and buccal cellsusing a discriminant function analysis of IFC measurements with anaverage classification accuracy of ˜94%. Ultimately, cellularmeasurements such as these, which can be obtained non-destructively,will provide probative information for many types of biological samplesand complement results from standard genetic profiling techniques.

Methods

Sample collection and preparation. Buccal and epidermal samples wereobtained from male and female volunteers pursuant to the VirginiaCommonwealth University Institutional Review Board (VCU-IRB) approvedprotocol ID #HM20000454_CR3. Written informed consent was obtained fromall participants for this study. For buccal samples, ten volunteers wereasked to swab the inside of cheek for 30 seconds. Swabs were left to dryfor between 24 hours and 6 days. Dried and fresh swabs were processed inthe same manner. For epidermal samples, ten individuals (six of whomwere buccal cell donors) were asked to hold/rub a conical tube (P/N229421; Celltreat Scientific; Pepperell, Mass.) for five minutes todeposit cells. Tubes were then left out for 24 hours to 5 days to drybefore collecting cells. Cells were collected from the surface with onesterile, pre-wetted swab, and one sterile, dry swab.

Vaginal cell samples were obtained from an existing sample repository atVirginia Commonwealth University. Samples were collected pursuant toVCU-IRB approved protocol ID #HM20002931_Ame2. Volunteers were asked toswab the inside of the vaginal cavity, and swabs were dried and storedat room temperature until analysis. Storage times ranged from 72 hoursto approximately eight weeks.

All collection swabs were eluted in 1 mL of 1× Cell Staining Buffer (P/N420201; Biolegend; San Diego, Calif.), and gently vortexed for 10seconds. Samples were centrifuged at 1500×g at 4° C. for 5 minutes. Thesupernatant was discarded, and the cell pellets were dissolved in 100 uLof 1× Cell Staining Buffer for imaging flow cytometry. A list of alldonor samples used in this study and their respective drying times areprovided in Table 1. The IRB approved protocols required the donors toconfirm that they were over 18 years of age, but did not require thattheir age be recorded.

TABLE 1 Tissue type and drying time for each sample. Buccal ContactEpidermal Vaginal Sample Time Sample Time Sample Time # Dried # Dried #Dried I66 24 hrs L49 24 hrs 1031 72 hrs L49 24 hrs N08 24 hrs 2368 5days R47 48 hrs S95 24 hrs 4017 6 days 5034 48 hrs I66 72 hrs 1022 7days C58 72 hrs K36 4 days 1028 7 days 5001 4 days P22 4 days 4502 12days Y60 5 days R47 4 days 4504 12 days Z32 5 days Y60 5 days 5021 14days N08 6 days Z32 5 days 5020 16 days B21 6 days Q17 5 days 5005 8weeks

Imaging Flow Cytometry and statistical analysis. All samples wereanalyzed using an Amnis® Imagestream X Mark II imaging flow cytometer(EMD Millipore; Burlington, Mass.) equipped with 405 nm, 488 nm, 561 nm,and 642 nm lasers. Laser voltages for all tests were set at 120 mW, 100mW, 100 mW and 150 mW, respectively. Images of individual events werecaptured in five detector channels labeled: 1 (430-505 nm), 2 (505-560nm), 3 (560-595 nm), 5 (640-745 nm), and 6 (745-780 nm). Channel 4 wasused to capture Brightfield images. Magnification was set at 40× andautofocus was enabled so that the focus varied with cell size. Aspectratio and area values for samples of each cell type are comparable toforward scatter/side scatter measurements collected with conventionalflow cytometry instrumentation. Raw image files (.rif) were thenimported into IDEAS® design software (EMD Millipore; Burlington, Mass.).Display Width and Display Height were changed to 120×120 pixels for eachimage. The ‘Shape Change Wizard’ option in the software was used toselect focused cells on a Gradient RMS_M04Ch04×Normalized Frequencyhistogram. Once the data was filtered for focused cells, single cellswere selected on an Area_M04×Aspect Ratio_M04 scatterplot. This was toensure that cell aggregates were not incorporated into the downstreamanalysis.

Data for individual cell events were collected for 17 differentfeatures: area, aspect ratio, aspect ratio intensity, contrast,intensity, mean pixel, median pixel, max pixel, length, width, height,brightness detail intensity (‘R3’ pixel increment), raw centroid X, rawcentroid Y, and circularity. These feature measurements were collectedacross multiple detector channels (i.e., fluorescence and brightfieldwavelengths) with the exception of measurements that could only bedetermined from brightfield images such as centroid X/Y and circularity.This yielded a total of 88 measurements/variables collected for eachcell. Cell yield varied across each of the study samples but did notappear to be correlated with tissue type, drying time, or individualdonor. Most cell populations yielded between 200 and 400 cell imageswith nine samples providing between 80 and 200 images.

IFC measurement values were then imported into SPSS® v23 statisticalsoftware (IBM, Inc. Chicago, Ill.). Differences in mean values betweenthe three cell types were tested using a one-way ANOVA analysis with aTukey HSD post-hoc test. Next, multivariate differences among the threecell type groups were analyzed using a Discriminant Function Analysis(DFA) based on the within-group covariance matrix. Results wereinitially compared from direct analysis of IFC measurements and thoseobtained from transforming the data first into principal components(PCs) and then conducting DFA on the PC scores. It was found that thelatter approach led to less differentiation in the canonical variateplot and poorer classification accuracy and thus direct analysis of rawmeasurements was used. Initially all data from all collected variableswere tested for cell type differentiation. Small sets of variables (<5)were then systematically excluded to investigate whether groupseparation in the canonical variate plot and classification accuracyimproved. This was done iteratively until a final set/combination ofvariables (88 total) was identified that resulted in the greatest degreeof separation in the canonical variate plot and the highest rate ofaccurate classifications.

Results

It was first determined whether IFC could be used to distinguish cellsfrom the three different epithelial tissue sources. During imagecollection and processing, some general qualitative differences betweenimages from each of the three cell types were noted. For example,circular features with a size consistent with nuclei (˜8 μm), wereobserved in the center of many of the buccal cells and vaginal cells(e.g., Images 1507, 1796, respectively, FIG. 2), while they were rarelyobserved in epidermal cell images. The presence of nuclei could be usedto confirm the presence of buccal or vaginal cells but is not a requiredaspect of exemplary embodiments disclosed herein. Buccal and vaginalcells were generally larger in size, >40 μm compared to epidermal cells,which were ˜20-50 μm although some size overlap between cell sources wasnoted. This could be due in part from the folding or degradation ofbuccal and vaginal cells during drying or sampling prior to IFC.Epidermal cells generally exhibited higher contrast features inbrightfield images compared to buccal or vaginal cells.

For the 264 pairwise comparisons between group means (88 variables andthree sample groups), only 42 yielded p-values greater than 0.01, withthe vast majority showing p values less than 0.0001. Of note weredifferences in means for circularity (7.8 epidermal, 4.1 buccal, 4.3vaginal), intensity (e.g., in 430-505 nm channel 3×10⁵ RFU epidermal,6×10⁴ RFU buccal, 5×10⁴ RFU vaginal), and brightness detail (e.g., in403-505 nm channel 1×10⁴ RFU epidermal, 9×10³ buccal, 7×10³ RFUvaginal). However, the range of values for each cell group showed a highdegree of overlap across the three cell types. Similarly, most variablesshowed large standard deviations for each cell type, with coefficientsof variation for individual measurements ranging from ˜20% to more than280%.

In order to determine whether the observed variation in IFC measurementscould be used to differentiate cell types, Discriminant FunctionAnalysis (DFA) was employed as a supervised multivariate technique tomodel variation between groups. In DFA, linear combinations of theoriginal variables are constructed (i.e., canonical variates) such thatthe variation between user-defined sample groups is maximized and withingroup variation is minimized. DFA is a well-established technique withdemonstrated applications for other forensic signature systems [10-12].For this dataset, the primary advantages of DFA are that differences inmeasurement scales across variables do not impact the analysis and it isrelatively robust to non-normally distributed data [13]. Additionally,the canonical variates generated with DFA can be used to classifyindividual samples into one of the user-defined groups. For this study,DFA was used to initially examine multivariate differences betweengroups. A DFA plot of all IFC measurements from the three cell typesshowed distinct separation between buccal, epidermal, and vaginal cellpopulations (FIG. 3). Multivariate differences between groups werestatistically significant, Wilk's Lambda=0.114, p<0.001. Some overlap isobserved among the sample groups on the DFA plot, in particular betweenbuccal and vaginal cell groups. A leave-one-out (LOO) classification onindividual cell images for each of the three groups and all 30 cellpopulations showed an overall classification accuracy of ˜90%.

Next algorithms constructed based on a discriminant function analysisframework were used to classify entire donor cell populations into oneof the three cell groups in a blinded fashion to determine the accuracyand robustness of this approach for identifying cell types from anunknown forensic sample. This was accomplished by withholding a givendonor cell population from the DFA and classifying each cell image intoone of the three epithelial cell types based on information from theremaining contributor cell populations. In general, epidermal cellsshowed the highest overall classification accuracy (88%) with six of theten donor cell populations having accuracies over 90%. Only one cellpopulation, P22, was below 80%. Buccal and vaginal cell populationsyielded lower overall classification rates, 72% and 75% respectively.Interestingly, classification accuracies were highly variable acrossindividual cell populations for these two groups, with buccal cellsranging between 24% and 96% and vaginal cells ranging between 26% and95%.

In an attempt to improve the classification accuracy for each cell type,individual cell populations were also tested with two-groupclassification schemes where one tissue group was excluded completelyfrom the analysis, i.e., buccal cells against epidermal cells; vaginalcells against epidermal cells; and buccal cells against vaginal cells.Simplified classification schemes could be run subsequent to theoriginal classification to help identify samples assigned to one of theclosely related sample groups, i.e., a cell image classified as a buccalcell in the three group DFA could then be run against a two group DFAcontaining only buccal and vaginal cells. Additionally, two groupcomparisons could approximate caseworking scenarios in which one of theepithelial cell types could be ruled out a fortiori for an unknown cellpopulation. Results from two-group DFA generally showed improvedclassification accuracy. Buccal and epidermal cell populations could bedifferentiated with the highest accuracy (˜94%). The lowestclassification rate of individual donor cell populations in thiscomparison was 80% (P22, Epidermal) with the majority of cellpopulations exhibiting classification accuracy of 95% or higher. Thevaginal-epidermal cell classifications showed comparable results with anoverall classification accuracy of ˜91%. Two individual cell populationsin this scheme exhibited markedly lower success rates (P22 epidermal 63%and 5005 vaginal 32%). However, the remaining cell populations hadclassification accuracy>80% with the majority>95%. Less differentiationwas observed between buccal and vaginal cells with an overallclassification accuracy of 78%. Seven donor cell populations stillshowed accuracies greater than 95% and three donor cell populations werebelow 60% accuracy (e.g., 5034, Buccal; 2368 Vaginal; 1028 Vaginal).

To investigate whether the DFA classification scheme can accuratelyassess the proportion of cell types in a two-person mixture, simulatedmixtures were created by randomly sampling two donors' cell images.These images were then classified into cell types using the remainingcontributor cell populations as the reference dataset for DFA. A 1:1simulated mixture consisting of L49 (epidermal) and B21 (buccal) cellimages was classified as 50% epidermal cells and 46% buccal cells, withthe remaining 4% of images classifying as vaginal cells. Using thetwo-group classification scheme, the cell population was determined tobe 50% epidermal cells and 50% buccal cells. Similar results wereobtained for a 1:1 simulated mixture consisting of Q17 (epidermal) and1031 (vaginal) cell images, with the population characterized as 49%epidermal cells, 49% vaginal cells and 2% buccal cells. The two groupclassification scheme estimated a cell population of 50% epidermal cellsand 50% vaginal cells. Mixtures containing contributor populations thatdemonstrated lower classification accuracy in earlier experiments hadlower success rates. For example, a 1:1 simulated mixture consisting ofC58 (buccal) and R47 (epidermal) cell images classified as 42% epidermalcells, 51% buccal cells, and 7% vaginal cells.

Discussion

Overall, the relatively high classification accuracy of epidermal cellsagainst buccal cells and epidermal cells against vaginal cells (>90%)suggests that systematic differences in morphological and/or opticalproperties measured by IFC can be used to distinguish between epithelialcell types in these comparison schemes (i.e., IFC using all 88 variableswith different weights). Further, measurement values can be used toconstruct an analysis framework for characterizing unknown cellpopulations into one of these three sample groups. The observedvariation between sloughed epidermal cells and buccal/vaginal cells isconsistent with the intrinsic biochemical, structural, and morphologicaldifferences for cells originating from each tissue source. For example,shed epidermal cells are derived from the stratum corneum andcharacterized by a high degree of keratinization with few if anyorganelles and little intracellular DNA owing to the apoptotic processesoccurring as cells migrate from the basal to the upper layers of theepidermis [15]. In contrast, buccal and vaginal cells are derived fromless stratified epithelial tissue and may be only partially keratinizedor unkeratinized. Although no studies to date have explicitly surveyedcellular differences between these three tissue sources usingfluorescence signatures, previous work has shown that changes incellular autofluorescence can be used to differentiate layers ofepidermal tissue with different intracellular components (e.g., keratin,tryptophan, FAD) [16,17]. Additionally, the morphological and sizedifferences detected with IFC (e.g., area and circularity measurements)are consistent with histological context of each cell type, i.e., shedepidermal cells hexagonal and ˜20-50 μm, while buccal and vaginal cellsare typically >40 μm with elongated shapes [18,19].

The overlap between cell sources shown in FIG. 3 and misclassificationsof individual cell images may be impacted by a number of factors. First,some similarities in fluorescence and/or morphological attributes areexpected, particularly for buccal and vaginal cells given that both arederived from non-keratinized epithelial tissue. This is consistent withpoorer classification accuracy of buccal-vaginal cell comparisonsrelative to buccal-epidermal and vaginal-epidermal. Second, cellpopulations in this data set represent a wide range of drying/exposuretimes prior to sampling and analysis. Levels of intrinsic fluorescenceare likely to change with time owing to the degradation of cellularcomponents such that specimens with longer periods of environmentalexposure may be harder to distinguish from each other. There were noclear relationships between exposure time and misclassification rate orposition on the DFA plot (FIG. 3). An analysis of buccal cellpopulations from two donors, each aged for 3, 24, 48, and 72 hours,suggests that fluorescence and/or morphological features may change in acharacteristic way over time (FIG. 4A-B). For example, the averageintensity of autofluorescence for buccal cells and blood cells increasesover a certain period of time between 1 day and 7 days. Then after 2weeks, there is not a clear trend in autofluorescence over time butthere is some indication that it may undergo some change.

Another factor that could be contributing to misclassifications isinter-individual variation. Previous work has shown thatautofluorescence signatures in shed epidermal cells can vary betweencontributors, likely owing to the presence of exogenous materialsassociated with the cell [20]. Cell populations from differentcontributors of the same tissue type (epidermal or buccal) and dryingtime (24 or 48 hours, respectively) showed some separation in a DFA(FIG. 5A-B). Increasing the number of unique donor cell populations inthe reference/comparison dataset may increase the isolation of anytissue-specific signatures that are present. Nevertheless,contributor-specific variation in IFC measurements may be used forestimating the number of individual cell populations in a biologicalsample and/or facilitating front-end cell separation in a DNA profilingworkflow.

It should also be noted that earlier studies have suggested thatsex-specific differences in the size and morphology of epidermal cellsmay exist [18]. Although there were no obvious differences inclassification accuracy or position on the DFA plot across male andfemale donors, IFC could be a viable approach for systematically testingfor sex specific signatures in a larger dataset of epidermal cellpopulations.

The goal of this study was to conduct an assessment of high-throughputanalysis of autofluorescence and morphological signatures and itsapplications for characterizing epithelial cell types in an unknownbiological sample. An important aspect of this workflow is thatintrinsic properties of the cell are being analyzed and no biochemicalor immunological stains or probes are required. High-throughput, singlecell measurements combined with a multivariate classification frameworkwere used to distinguish epidermal cells from other epithelial cellsources across a range of drying times with an overall high degree ofaccuracy. Although a range of factors may contribute to morphological oroptical properties in any given sample (e.g., individual-specificsignatures and degradation time), these results suggest thatmultivariate approaches may be used to extract tissue-specificsignatures from biological samples.

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While the invention has been described in terms of its preferredembodiments, those skilled in the art will recognize that the inventioncan be practiced with modification within the spirit and scope of theappended claims. Accordingly, the present invention should not belimited to the embodiments as described above, but should furtherinclude all modifications and equivalents thereof within the spirit andscope of the description provided herein.

What is claimed is:
 1. A method of characterizing cells from an unknowncontributor or contributors in a forensic sample, comprising obtaining aplurality of morphological and/or autofluorescence measurements from aplurality of cells in the forensic sample; classifying the plurality ofcells into three or more groups using two or more binaryclassifications, each binary classification comprising calculating atleast two coordinate values for each cell using respective first andsecond functions that are weighted combinations of the plurality ofmorphological and/or autofluorescence measurements, and sorting eachcell into either a first group or a second group based on ratios ofmultivariate distances between the calculated coordinate values andmultivariate centroids of cell groups in a reference dataset, whereinthe three or more groups includes the first group and/or second group;and outputting information for the three or more groups based on theclassifying step.
 2. The method of claim 1, wherein the two or morebinary classifications are performed successively, wherein for any twobinary classifications in immediate succession, only the cells sortedinto the second group by the first binary classification are subjectedto the second binary classification.
 3. The method of claim 2, whereinthe respective functions of the two or more binary classificationscontain the same plurality of morphological and/or autofluorescencevariables but different weightings.
 4. The method of claim 1, furthercomprising using the output information for downstream DNA profilingand/or crime reconstruction.
 5. The method of claim 1, wherein the threeor more groups are all epithelial cell types.
 6. The method of claim 5,wherein the three or more groups comprise epidermal, buccal, andvaginal.
 7. The method of claim 6, wherein the two or more binaryclassifications include a first binary classification thatdifferentiates epidermal cells from non-epidermal cells and a secondbinary classification that differentiates buccal cells from vaginalcells, wherein the second binary classification is performed only forcells classified by the first binary classification as non-epidermalcells.
 8. The method of claim 1, further comprising counting a totalcell count for each of the three or more groups after all binaryclassifications are complete.
 9. The method of claim 1, wherein the stepof obtaining comprises generating images of individual cells andanalyzing the images to obtain the plurality of morphological and/orautofluorescence measurements.
 10. The method of claim 9, wherein themeasurements are obtained with an imaging flow cytometer.
 11. The methodof claim 1, wherein the one or more morphological and/orautofluorescence measurements are selected from the group consisting ofarea, aspect ratio, aspect ratio intensity, contrast, fluorescenceintensity, mean pixel, median pixel, max pixel, length, width, height,brightness detail intensity (‘R3’ pixel increment), raw centroid X, rawcentroid Y, and circularity.
 12. A method of training a computer foranalysis of forensic samples, comprising obtaining for a plurality ofmorphological and/or autofluorescence variables a plurality ofmeasurements from a plurality of cells having one or more knowncharacteristics; and generating two or more functions which are weightedcombinations of the plurality of morphological and/or autofluorescencevariables such that the variation between user-defined sample groups ismaximized and within group variation is minimized, the two or morefunctions being usable or used to classify or characterize further cellsfrom an unknown contributor.
 13. The method of claim 12, wherein themeasurements are obtained with an imaging flow cytometer.
 14. The methodof claim 12, wherein the one or more morphological and/orautofluorescence measurements are selected from the group consisting ofarea, aspect ratio, aspect ratio intensity, contrast, intensity, meanpixel, median pixel, max pixel, length, width, height, brightness detailintensity (‘R3’ pixel increment), raw centroid X, raw centroid Y, andcircularity.
 15. The method of claim 12, wherein the one or more knowncharacteristics are selected from the group consisting of cell type;time since cell deposition; and age, sex, and ethnicity of cellcontributor.
 16. A device comprising one or more processors and acomputer readable storage medium, the computer readable storage mediumhaving instructions executable by the one or more processors forcharacterizing cells from an unknown contributor or contributors in aforensic sample, said instructions when executed causing the device toperform: obtaining a plurality of morphological and/or autofluorescencemeasurements from a plurality of cells in the forensic sample; with theone or more processors, classifying the plurality of cells into three ormore groups using two or more binary classifications, each binaryclassification comprising calculating at least two coordinate values foreach cell using respective first and second functions that are weightedcombinations of the plurality of morphological and/or autofluorescencemeasurements, and sorting each cell into either a first group or asecond group based on ratios of multivariate distances between thecalculated coordinate values and multivariate centroids of cell groupsin a reference dataset, wherein the three or more groups includes thefirst group and/or second group; and outputting information for thethree or more groups based on the classifying step.
 17. The device ofclaim 16, wherein the two or more binary classifications are performedsuccessively, wherein for any two binary classifications in immediatesuccession, only the cells sorted into the second group by the firstbinary classification are subjected to the second binary classification.18. The device of claim 17, wherein the respective functions of the twoor more binary classifications contain the same plurality ofmorphological and/or autofluorescence variables but differentweightings.
 19. The device of claim 16, wherein the three or more groupsare all epithelial cell types.
 20. The device of claim 19, wherein thethree or more groups comprise epidermal, buccal, and vaginal.
 21. Thedevice of claim 20, wherein the two or more binary classificationsinclude a first binary classification that differentiates epidermalcells from non-epidermal cells and a second binary classification thatdifferentiates buccal cells from vaginal cells, wherein the secondbinary classification is performed only for cells classified by thefirst binary classification as non-epidermal cells.
 22. The device ofclaim 16, further comprising counting a total cell count for each of thethree or more groups after all binary classifications are complete. 23.The device of claim 16, wherein the step of obtaining comprisesgenerating images of individual cells and analyzing the images to obtainthe plurality of morphological and/or autofluorescence measurements. 24.The device of claim 23, wherein the measurements are obtained with animaging flow cytometer.
 25. The device of claim 16, wherein the one ormore morphological and/or autofluorescence measurements are selectedfrom the group consisting of area, aspect ratio, aspect ratio intensity,contrast, intensity, mean pixel, median pixel, max pixel, length, width,height, brightness detail intensity (‘R3’ pixel increment), raw centroidX, raw centroid Y, and circularity.